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Technology is changing all the time, and the jobs that are associated with technology are changing as well. People that are looking at careers in technology will see some jobs that are being phased out as others increase in popularity. This means that people that are interested in tech jobs should be vigilant in researching those opportunities that are growing in demand.

A Dying Breed of Legacy Systems

The mainframe programmers i.e., COBOL, have been getting phased out for years, and are reaching retirement age. The demand for these skills are at an end of an era as more technology surfaces with needs for app development and cloud migration. These jobs will be phased out and are being replaced with developers that are knowledgeable in more object-oriented programming positions such as Java, C#, and etc.

Programmers / Analysts

Professionals that work in software development, can find work in a number of different tech careers. People that know how to program, particularly in object oriented programming, can expect to be employed and in demand for some time to come. The salaries for programmers range from $50k – millions, depending on the skillsets one has mastered. Like any other profession, one can opt to learn just enough to get by or hone in on a discipline currently in demand and master it, such as data analysts, machine learning analyst and cloud migration specialists.

Tech Support for Portable Devices

The healthcare industry is seeing a rise in jobs in Information Technology because more hospitals are going paperless. There is a great demand for people that have the ability to work with portable devices because this is what many doctors and nurses will be using as they move away from the long paper trail that has been created from patients. People that have the ability to configure and troubleshoot portable devices like tablets and phones are able to support the applications for these devices will be in high demand. In this case, learning programming languages such a C++ is the perfect route to go in.

Technology Trainers

There will always be a need for someone that can learn, utilize and teach proprietary programs to others. Internal proprietary technology will need to be updated which means that technology trainers are expected to be current in the knowledge base for companies that are utilizing this software. People that are in the training field, will need to stay updated with new technology, grasp new concepts quickly and be able to teach it efficiently. As more companies take hold of proprietary programs, it becomes important for software application trainers to be put in place to teach this technology.

Printer Support Jobs Dwindle

People that are working in the technology field of printer support will need to consider looking at other opportunities because some of these printer support jobs will be phased out. There is a reason for this. More jobs are becoming phased out in the world of printer technology because fewer people are using printers. It has become easier to read the documents and transfer these documents to other workers inside an organization. This means that less money is being spent on printers. Even less money is being spent on printer support. People that have acquired jobs where their primary role is to work in printer repair will see a decline in the number of people that are needed for these types of positions. It becomes a lot less feasible to have printer repair people in place when there is no printer in the office.

Graphic Design

Technology also holds a special place for those that have the experience in graphic design. Websites and social media really engage people in visual art and people that know how to display it on web pages have a plethora of jobs. This leaves this field wide open for those that know about design structures and editing images that can result in eye catching imagery.

Wan/ Lan Management

One big area that offers an array of different jobs is the area of wide and local network router and switch management. People that are proficient in programming switches and building networks can get themselves a number of jobs dealing with the network topology. Tech careers are booming when it comes to this type of field because many people do not have this experience. They may know how to set up computers, but they may not have any idea about what to do if the network connection is no longer working properly.

People that know how to configure switches for networks and troubleshoot these issues with network connectivity will have a wide range of geographical locations that they connect together to build one network for a business.

A business rule is the basic unit of rule processing in a Business Rule Management System (BRMS) and, as such, requires a fundamental understanding. Rules consist of a set of actions and a set of conditions whereby actions are the consequences of each condition statement being satisfied or true. With rare exception, conditions test the property values of objects taken from an object model which itself is gleaned from a Data Dictionary and UML diagrams. See my article on Data Dictionaries for a better understanding on this subject matter.

A simple rule takes the form:

ifcondition(s)

thenactions.

An alternative form includes an else statement where alternate actions are executed in the event that the conditions in the if statement are not satisfied:

ifcondition(s)

thenactions

else alternate_actions

It is not considered a best prectice to write rules via nested if-then-else statements as they tend to be difficult to understand, hard to maintain and even harder to extend as the depth of these statements increases; in other words, adding if statements within a then clause makes it especially hard to determine which if statement was executed when looking at a bucket of rules. Moreoever, how can we determine whether the if or the else statement was satisfied without having to read the rule itself. Rules such as these are often organized into simple rule statements and provided with a name so that when reviewing rule execution logs one can determine which rule fired and not worry about whether the if or else statement was satisfied. Another limitation of this type of rule processing is that it does not take full advantage of rule inferencing and may have a negative performance impact on the Rete engine execution. Take a class with HSG and find out why.

In recent decades, companies have become remarkably different than what they were in the past. The formal hierarchies through which support staff rose towards management positions are largely extinct. Offices are flat and open-plan collaborations between individuals with varying talent who may not ever physically occupy a corporate workspace. Many employed by companies today work from laptops nomadically instead. No one could complain that IT innovation hasn’t been profitable. It’s an industry that is forecasted to rake in $351 billion in 2018, according to recent statistics from the Consumer Technology Association (CTA). A leadership dilemma for mid-level IT managers in particular, however, has developed. Being in the middle has always been a professional gray area that only the most driven leverage towards successful outcomes for themselves professionally, but mid-level managers in IT need to develop key skills in order to drive the level of growth that the fast paced companies who employ them need.

What is a middle manager’s role exactly?

A typical middle manager in the IT industry is usually someone who has risen up the ranks from a technical related position due to their ability to envision a big picture of what’s required to drive projects forward. A successful middle manager is able to create cohesion across different areas of the company so that projects can be successfully completed. They’re also someone with the focus necessary to track the progress of complex processes and drive them forward at a fast pace as well as ensure that outcomes meet or exceed expectations.

What challenges do middle managers face in being successful in the IT industry today?

While middle managers are responsible for the teams they oversee to reach key milestones in the life cycle of important projects, they struggle to assert their power to influence closure. Navigating the space between higher-ups and atomized work forces is no easy thing, especially now that workforces often consist of freelancers with unprecedented independence.

What are the skills most needed for an IT manager to be effective?

Being educated on a steady basis to handle the constant evolution of tech is absolutely essential if a middle manager expects to thrive professionally in a culture so knowledge oriented that evolves at such a rapid pace. A middle manager who doesn't talk the talk of support roles or understand the nuts and bolts of a project they’re in charge of reaching completion will not be able to catch errors or suggest adequate solutions when needed.

How has the concept of middle management changed?

Middle managers were basically once perceived of as supervisors who motivated and rewarded staff towards meeting goals. They coached. They toggled back and forth between the teams they watched over and upper management in an effort to keep everyone on the same page. It could be said that many got stuck between the lower and upper tier of their companies in doing so. While companies have always had to be result-oriented to be profitable, there’s a much higher expectation for what that means in the IT industry. Future mid-level managers will have to have the same skills as those whose performance they're tracking so they can determine if projects are being executed effectively. They also need to be able to know what new hires that are being on-boarded should know to get up to speed quickly, and that’s just a thumbnail sketch because IT companies are driven forward by skills that are not easy to master and demand constant rejuvenation in the form of education and training. It’s absolutely necessary for those responsible for teams that bring products and services to market to have similar skills in order to truly determine if they’re being deployed well. There’s a growing call for mid-level managers to receive more comprehensive leadership training as well, however. There’s a perception that upper and lower level managers have traditionally been given more attention than managers in the middle. Some say that better prepped middle managers make more valuable successors to higher management roles. That would be a great happy ending, but a growing number of companies in India’s tech sector complain that mid-level managers have lost their relevance in the scheme of the brave new world of IT and may soon be obsolete.

Machine learning systems are equipped with artificial intelligence engines that provide these systems with the capability of learning by themselves without having to write programs to do so. They adjust and change programs as a result of being exposed to big data sets. The process of doing so is similar to the data mining concept where the data set is searched for patterns. The difference is in how those patterns are used. Data mining's purpose is to enhance human comprehension and understanding. Machine learning's algorithms purpose is to adjust some program's action without human supervision, learning from past searches and also continuously forward as it's exposed to new data.

The News Feed service in Facebook is an example, automatically personalizing a user's feed from his interaction with his or her friend's posts. The "machine" uses statistical and predictive analysis that identify interaction patterns (skipped, like, read, comment) and uses the results to adjust the News Feed output continuously without human intervention.

Impact on Existing and Emerging Markets

The NBA is using machine analytics created by a California-based startup to create predictive models that allow coaches to better discern a player's ability. Fed with many seasons of data, the machine can make predictions of a player's abilities. Players can have good days and bad days, get sick or lose motivation, but over time a good player will be good and a bad player can be spotted. By examining big data sets of individual performance over many seasons, the machine develops predictive models that feed into the coach’s decision-making process when faced with certain teams or particular situations.

General Electric, who has been around for 119 years is spending millions of dollars in artificial intelligence learning systems. Its many years of data from oil exploration and jet engine research is being fed to an IBM-developed system to reduce maintenance costs, optimize performance and anticipate breakdowns.

Over a dozen banks in Europe replaced their human-based statistical modeling processes with machines. The new engines create recommendations for low-profit customers such as retail clients, small and medium-sized companies. The lower-cost, faster results approach allows the bank to create micro-target models for forecasting service cancellations and loan defaults and then how to act under those potential situations. As a result of these new models and inputs into decision making some banks have experienced new product sales increases of 10 percent, lower capital expenses and increased collections by 20 percent.

Emerging markets and industries

By now we have seen how cell phones and emerging and developing economies go together. This relationship has generated big data sets that hold information about behaviors and mobility patterns. Machine learning examines and analyzes the data to extract information in usage patterns for these new and little understood emergent economies. Both private and public policymakers can use this information to assess technology-based programs proposed by public officials and technology companies can use it to focus on developing personalized services and investment decisions.

Machine learning service providers targeting emerging economies in this example focus on evaluating demographic and socio-economic indicators and its impact on the way people use mobile technologies. The socioeconomic status of an individual or a population can be used to understand its access and expectations on education, housing, health and vital utilities such as water and electricity. Predictive models can then be created around customer's purchasing power and marketing campaigns created to offer new products. Instead of relying exclusively on phone interviews, focus groups or other kinds of person-to-person interactions, auto-learning algorithms can also be applied to the huge amounts of data collected by other entities such as Google and Facebook.

A warning

Traditional industries trying to profit from emerging markets will see a slowdown unless they adapt to new competitive forces unleashed in part by new technologies such as artificial intelligence that offer unprecedented capabilities at a lower entry and support cost than before. But small high-tech based companies are introducing new flexible, adaptable business models more suitable to new high-risk markets. Digital platforms rely on algorithms to host at a low cost and with quality services thousands of small and mid-size enterprises in countries such as China, India, Central America and Asia. These collaborations based on new technologies and tools gives the emerging market enterprises the reach and resources needed to challenge traditional business model companies.

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A successful career as a software developer or other IT professional requires a solid
understanding of software development processes, design patterns, enterprise application architectures,
web services, security, networking and much more. The progression from novice to expert can be a
daunting endeavor; this is especially true when traversing the learning curve without expert guidance. A
common experience is that too much time and money is wasted on a career plan or application due to misinformation.

The Hartmann Software Group understands these issues and addresses them and others during any
training engagement. Although no IT educational institution can guarantee career or application development success,
HSG can get you closer to your goals at a far faster rate than self paced learning and, arguably, than the competition.
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